TY - JOUR A1 - Schäfer, Merlin A1 - Menz, Stephan A1 - Jeltsch, Florian A1 - Zurell, Damaris T1 - sOAR: a tool for modelling optimal animal life-history strategies in cyclic environments JF - Ecography : pattern and diversity in ecology ; research papers forum N2 - Periodic environments determine the life cycle of many animals across the globe and the timing of important life history events, such as reproduction and migration. These adaptive behavioural strategies are complex and can only be fully understood (and predicted) within the framework of natural selection in which species adopt evolutionary stable strategies. We present sOAR, a powerful and user-friendly implementation of the well-established framework of optimal annual routine modelling. It allows determining optimal animal life history strategies under cyclic environmental conditions using stochastic dynamic programming. It further includes the simulation of population dynamics under the optimal strategy. sOAR provides an important tool for theoretical studies on the behavioural and evolutionary ecology of animals. It is especially suited for studying bird migration. In particular, we integrated options to differentiate between costs of active and passive flight into the optimal annual routine modelling framework, as well as options to consider periodic wind conditions affecting flight energetics. We provide an illustrative example of sOAR where food supply in the wintering habitat of migratory birds significantly alters the optimal timing of migration. sOAR helps improving our understanding of how complex behaviours evolve and how behavioural decisions are constrained by internal and external factors experienced by the animal. Such knowledge is crucial for anticipating potential species’ response to global environmental change. Y1 - 2017 U6 - https://doi.org/10.1111/ecog.03328 SN - 0906-7590 SN - 1600-0587 VL - 41 IS - 3 SP - 551 EP - 557 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Gopalakrishnan, Sathej A1 - Montazeri, Hesam A1 - Menz, Stephan A1 - Beerenwinkel, Niko A1 - Huisinga, Wilhelm T1 - Estimating HIV-1 fitness characteristics from cross-sectional genotype data JF - PLoS Computational Biology : a new community journal N2 - Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure. Y1 - 2014 U6 - https://doi.org/10.1371/journal.pcbi.1003886 SN - 1553-734X SN - 1553-7358 VL - 10 IS - 11 PB - PLoS CY - San Fransisco ER -